We optimize all models using Adam optimizer. Batch size is set as 2048 and learning rate is set as 0.001 on all three datasets.
For each training instance, we sample one interacted sample and one randomly sampled missing interaction to feed the model.
We use the default network settings of all models.
Besides, the embedding size of users and items in GMF and NeuMF, the hidden size of CDAE, are all set to 32.
For LightGCN, the embedding size is set to 64 without dropout [1]}. The \(L_2\)  regularization coefficient is tuned in \(\lbrace 0.01, 0.1, 1, 10, 100, 1000\rbrace \)  divided by the number of users in each dataset.
For DPI and DVAE, there are three hyper-parameters in total: \(C_1\) , \(C_2\)  and \(\alpha \) .
We apply a grid search for hyperparameters: \(C_1\)  and \(C_2\)  are searched among {1, 10, 100, 1000}, \(\alpha \)  is tuned in {0, 0.5, 1}.
Note that the hyperparameters of recommendation models keeps exactly the same across all training approaches for a fair comparison. We use MF as \(h\)  and \(h^{\prime }\)  in Eq.(REF ) without special mention.
